Post-processing a multi-spectral image for enhanced object identification

ABSTRACT

What is disclosed is a system and method for post-processing a multi-spectral image which has already been processed for pixel classification. A binary image is received which contains pixels that have been classified using a pixel classification method. Each pixel in the image has an associated intensity value and has a pixel value of 1 or 0 depending on whether the pixel has been classified as a material of interest or not. A block of size m×n is defined. Pixel values in a block are changed according to a threshold-based filtering criteria such that pixels in the same block all have the same binary value. The block is then shifted by k pixels and pixel processing repeats until all pixels have been processed. Once all blocks have been processed, contiguous pixels having the same binary value are grouped to form objects. In such a manner, pixel classification errors are reduced.

CROSS REFERENCE TO RELATED CASES

This case is related to U.S. patent application Ser. No. 13/324,368,entitled: “Post-Processing A Multi-Spectral Image For Enhanced ObjectIdentification”, to Wang et al.

TECHNICAL FIELD

The present invention is directed to methods for post-processing amulti-spectral image which has been pre-processed via a pixelclassification method such that objects in the image are more correctlyidentified.

BACKGROUND

While methods which classify pixels in an infrared image correctlyclassify most pixels, incorrect classifications are still assigned tosome pixels when the source image was captured by the multi-band imagingsystem under low light conditions. With information such as, forinstance, the illumination spectrum, windshield transmittance, filtertransmittance, detector response, and the reflectance of a knownmaterial, a theoretical camera intensity for a pixel of that material inthe image can be calculated. By correlating the captured cameraintensity with the theoretical camera intensity for that material, apixel can be classified as being that material or not. Theoretically,pixels can be classified correctly. However, in reality, many pixels arewrongly classified due to non-uniform lighting, background fluctuation,and objects such as a shadow. FIG. 15 shows a binary image which hasbeen pixel-classified as to skin using a correlation coefficient methodof pixel classification. The binary image of FIG. 15 was generated usinga threshold for the correlation coefficient of 0.94 and assigning avalue of 1 to a pixel if the correlation coefficient for that pixel wasgreater than 0.94, and assigning a value of 0 to that pixel if thecorrelation coefficient was less than 0.94. As shown, many pixels havebeen wrongly classified as correlating to human skin. As such,post-processing of a pixel-classified binary image is needed to reducepixel classification error.

Accordingly, what is needed in this art are sophisticated systems andmethods for post-processing a multi-spectral image which has alreadybeen processed for pixel classification so that the objects in the imagecan be properly identified and extracted.

INCORPORATED REFERENCES

The following U.S. Patents, U.S. Patent Applications, and Publicationsare incorporated herein in their entirety by reference.

“Determining A Total Number Of People In An IR Image Obtained Via An IRImaging System”, U.S. patent application Ser. No. 12/967,775, by Wang etal, which discloses a ratio method for classifying pixels in an IRimage.

“Determining A Number Of Objects In An IR Image”, U.S. patentapplication Ser. No. 13/086,006, by Wang et al, which discloses acorrelation method and a best fitting reflectance method for classifyingpixels in an IR image.

“Method For Classifying A Pixel Of A Hyperspectral Image In A RemoteSensing Application”, U.S. patent application Ser. No. 13/023,310, byMestha et al.

BRIEF SUMMARY

What is disclosed is a system and method for post-processing amulti-spectral image which has been processed for pixel classificationso that objects in the image can be properly identified. Disconnectedsmall areas of pixels are filtered using the methods disclosed herein.The present method is intended to be used as an adjunct to pixelclassification methods when an image is captured using a multibandimaging system. In such a manner, a more accurate pixel classificationis effectuated.

One embodiment of the present method for determining objects in an imagecaptured with a multi-band infrared imaging system involves thefollowing. First, a binary image is received which contains pixels thathave been classified using a pixel classification method. Each pixel inthe image has an associated intensity value. Each of the pixels has apixel value of 1 or 0 depending on whether the pixel is classified as amaterial of interest or not. A block of pixels of size m×n is defined,where either: m≧2 or n≧2. The following steps are then performed. Instep A, a density value is calculated using intensity values collectedfor pixels in a block of pixels of the size. The block encompasses thecurrent pixel. In step B, the calculated density is then compared to apre-determined threshold value. In step C, if the density is less thanthe threshold, all pixels in this block are set to zero; otherwise allpixels in this block are set to a non-zero value. The block is thenshifted in the image by k pixels sequentially, where k is less than m orn. Steps (A)-(C) are repeated until all pixels in the image have beenprocessed. Thereafter, pixels which have a same binary value are groupedto form objects. The number of formed objects can then be counted in theimage.

Many features and advantages of the above-described method will becomereadily apparent from the following detailed description andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the subject matterdisclosed herein will be made apparent from the following detaileddescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 shows an example image captured of a person in an airport pullinga wheeled carrying device loaded with packages;

FIG. 2 shows various formed objects by having grouped pixels of theobjects of interest identified in FIG. 1;

FIG. 3 illustrates one example IR detection system 300;

FIG. 4 illustrates one example IR illumination system 400;

FIG. 5 plots reflectance values vs. wavelengths for human skin;

FIG. 6 is a flow diagram of one example embodiment of the present methodfor determining objects in an image captured with a multi-band imagingsystem;

FIG. 7 is a continuation of the flow diagram of FIG. 6 with flowprocessing continuing with respect to nodes A or B;

FIG. 8 shows a portion 800 of the image of FIG. 1 wherein a block ofpixels of size m×n has been defined in accordance with the teachingshereof;

FIG. 9 shows a result of the present method applied to the image of FIG.15;

FIG. 10 shows an original IR image of two persons in the front passengerseat of a motor vehicle;

FIG. 11 is a binary version of the image of FIG. 10 wherein the pixelshave been classified as to human tissue;

FIG. 12 shows a result of the present method applied to the image ofFIG. 11;

FIG. 13 is a block diagram of an example image post-processing system inaccordance with the teachings hereof wherein various aspects of thepresent method, as described with respect to the flow diagrams of FIGS.6 and 7, are performed;

FIG. 14 illustrates a block diagram of one example special purposecomputer for implementing various aspects of the present methoddescribed with respect to the flow diagrams of FIGS. 6 and 7 and themodules and processors of the block diagram of FIG. 13; and

FIG. 15 is a prior art binary image comprising pixels which have beenclassified as human skin using the correlation method.

DETAILED DESCRIPTION

What is disclosed is a system and method for post-processing amulti-spectral image which has been processed for pixel classificationso that objects in the image can be identified and extracted.

NON-LIMITING DEFINITIONS

A “material of interest”, as used herein, refers to a material whichpixels in an image have been classified as comprising. Example materialsof interest are human tissue (hair, skin, etc.), plastics, metals,composite materials, and the like. The list of materials that may be ofinterest will depend on the environment where the teachings hereof findtheir implementations. Such environments may be, for instance, anairport, a courthouse, a government office building, a vehicle occupancydetection system, to name a few. In one embodiment, pixels which havebeen classified as being a material of interest are assigned a valueof 1. Otherwise, pixels are assigned a value of 0. In such a manner, thesource image is processed, via pixel classification, into a binaryimage.

A “binary image” is an image containing pixels which have beenclassified using a pixel classification method. FIG. 1 illustrates anexample image 100 captured using a multi-band IR imaging system showinga person 102 pulling a wheeled luggage carrying device containing anassortment of packages. For explanatory purposes, the materials ofinterest for pixel classification purposes were human skin tissue and aplastic such as PET, HDPE or LDPE. In the pixel-classified image of FIG.1, pixels of facial region 103, right arm portion 104, and left armportion 105 will be classified as pixels which have been identified as amaterial of interest, i.e., skin tissue (material ‘A’). Pixels ofpackages 106, 107 and 108, will be classified as pixels which have beenidentified as a material of interest, i.e, the type of plastic (material‘B’). Upon classification, each pixel will have been assigned a value of1 if the pixel belongs to one of the materials of interest and a valueof 0 otherwise. In such a manner, the original image is transformed intoa binary image by having been processed using a pixel classificationtechnique. It should be appreciated that any numeric value can beassigned to a pixel which has been classified as a material of interest.For example, assume that pixels in a given image have been identified aseach of 3 different materials of interest. In this instance, one set ofpixels of a first object identified as belonging to a first material ofinterest may be assigned a value of 1, while pixels of a second objectidentified as belonging to a second material of interest may be assignedthe value of 2, and pixels of a third object identified as belonging toa third material of interest may have been assigned a value of 3. Inthis case, the binary image would be comprised of pixels identified asmaterials of interest would have respective assigned values of 1, 2 and3, and pixels in the image which do not belong to any of the materialsof interest would have a value of 0. Such alternative embodiments areintended to fall within the scope of the appended claims.

A “formed object” is an object formed by grouping together contiguouslyconnected pixels which all have the same binary value as a result ofhaving been classified as a material of interest. Grouped pixels areshown by way of example in FIG. 2 wherein object 203 was formed bygrouping together contiguous pixels of skin segment 103. Likewise,objects 204 and 205 were formed in a similar manner by grouping togetherpixels of skin segments 104 and 105, respectively. All of these pixelsare contiguous and all have been assigned the same binary value becauseeach has been classified as human tissue. Similarly, objects 206, 207and 208 were formed by grouping together contiguous pixels classified asa material of interest, i.e., plastic, and thus all have the same binaryvalue.

An “IR imaging system” is an apparatus comprising an IR illuminator andan IR detector designed to capture IR light reflected from a target,separate it into its component wavelengths, and output an IR image ofthat target. The IR image is captured over multiple wavelength bands ofinterest. An IR imaging system can be either a single IR detector and asequentially illuminated N-band illuminator (N≧3) with one fixed filter,or comprise a total of N detector (N≧3) each having a respective bandpass filter and a single illuminator. An example IR detection system isshown in FIG. 3. An example IR illumination system is shown in FIG. 4.

A “Correlation Method” refers to a method of pixel classificationwherein pixels of an IR image are classified as human tissue based uponan amount of correlation between a captured intensity of that pixel anda (scaled) intensity calculated from a model. The correlation methodusing a materials spectra database containing pre-measured reflectancesof known materials such as human skin and hair, and other materials ofinterest. The database includes the transmittance of windshield glass,the power spectra of the IR illuminator(s), the filter transmittances,and a responsivity curve of the IR detector(s). A theoretical pixelintensity for each object in the image is calculated and the measuredintensity of each pixel compared with the theoretical intensities todetermine an amount of correlation therebetween. If the intensity of apixel agrees with the measured intensity of the pixel of a known object,then the correlation will be high (close to 1). Otherwise, thecorrelation will be low (close to 0 or negative). Pixels are classifiedbased upon a comparison with a threshold value.

A “Ratio Method” is a pixel classification method classifies a pixel ashuman tissue vs. other materials if the ratio is larger or smaller thana predetermined threshold value.

A “Best Fitting Reflectance Method” is a pixel classification methodwherein each pixel in the IR image is processed and classified basedupon a best fitting reflectance by cross-referencing measured pixelreflectances with reflectances of known materials in a materialsspectral database and determining a best fitting reflectance. FIG. 5shows reflectances for human skin tissue. One materials spectraldatabase is the High-Resolution Transmission Molecular AbsorptionDatabase (HITRAN) maintained by the Atomic and Molecular PhysicsDivision of the Harvard-Smithsonian Center for Astrophysics. HITRAN isdownloadable from Harvard's website.

Example IR Detector

Reference is now being made to FIG. 3 which illustrates one embodimentof an example IR detection system 300 for use in accordance with theteachings hereof.

Image 316 reflects the IR output beam 414 emitted by focusing optics 412of the IR illumination system of FIG. 4. A portion of the reflected IRlight is received by optics 302 having lens 303 that focus the receivedlight onto sensor(s) 304 which spatially resolves the received light toobtain IR image 308. Optics 302 may also include one or more bandpassfilters that only allow light in a narrow wavelength band to pass thoughthe filter. The filters may also be sequentially changed to obtain Nintensities at 308. Sensor 304 sends the IR image information tocomputer 306 for processing and storage. Detector 308 is a multispectralimage detection device whose spectral content may be selectable througha controller (not shown). Detector 304 records light intensity atmultiple pixels locations along a two dimensional grid. Optics 302 anddetector 304 include components commonly found in various streams ofcommerce. Suitable sensors include charge-coupled device (CCD)detectors, complementary metal oxide semiconductors (CMOS) detectors,charge-injection device (CID) detectors, vidicon detectors, reticondetectors, image-intensifier tube detectors, pixelated photomultipliertube (PMT) detectors, InGaAs (Indium Gallium Arsenide), Mercury CadmiumTelluride (MCT), and Microbolometer. Computer 306 is in communicationwith optics 302 to control the lens thereof and is in communication withdetector 304 to control the sensitivity thereof. Computer 306 receivessensitivity values associated with each pixel of IR image 308. Computer306 includes a keyboard, monitor, printer, etc. (not shown) as arenecessary to effectuate a control of various elements of IR detectionsystem 300.

Example IR Illuminator

Reference is now being made to FIG. 4 which illustrates one embodimentof an example IR illumination system 400 for use in accordance with theteachings hereof.

The IR illumination system of FIG. 4 is shown comprising a plurality ofIR light sources 402 each emitting a narrow band of IR radiation at arespective peak wavelength (λ₁, . . . , λ_(n)). Light source 402 is anarray of light emitting diodes (LEDs). Each diode is pre-selected toemit radiation at a particular wavelength band of interest, and definesa source in the array for that wavelength band. Controller 108 iscoupled to source array 402 and controls the input current to eachilluminator and, thereby, the output intensity of each. Sensing optics404 has optics 403 which combine the wavelengths to produce IRillumination beam 406. Sensor 410 samples the radiation emitted from thearray of IR light sources and provides feedback to controller 408.Focusing optics 412 receives beam 406 and focuses output beam 414 ontoimage 316. Optics 412 includes a plurality of lens of varying focallengths positioned in the beam path to focus the beam as desired.Controller 408 is also coupled to optics 412 to effectuate changes inoutput beam 414 due to target size, target distance, target speed, toname a few constraints. Controller 408 is in communication with storagedevice 309 to store/retrieve calibration information, intensity levels,and the like, including data and machine readable program instructions.Controller 408 may comprise a computer system such as a desktop, laptop,server, mainframe, and the like, or a special purpose computer systemsuch as an ASIC. Controller 408 may be placed in wired or wirelesscommunication with a computing workstation over a network. Such anetwork may be a local area network (LAN) or the Internet. It should beappreciated that any of the components of IR illumination system 400 maybe placed in communication with such a computing system to facilitatethe intended purposes hereof.

Any of the optics described with respect to IR illumination system 400of FIG. 4 can be replaced with an optical system having optical powerand may further include mirrors. Such an optical system may includemultiple components each having optical power, e.g., it may be doubletor a triple lens. In the limit that such optical systems define a uniquefocal length F, the source array and grating would be positioned in thefront and back focal planes of the optics. As a result, the opticalsystem images the grating at infinity with respect to each element ofthe light source array and thus each source element sees the same regionof the grating. The light from each element would be coextensive on thatregion. The grating can then produce output radiation whose spectralcontent is substantially uniform across its transverse profile bycompensating for the dispersion associated with lateral position of thedifferent wavelength band sources. This, in turn, allows the spectralcontent of output beam 414 to be substantially uniform across itstransverse profile. In practice, it may be difficult to precisely definea desired focal length for the optical system because of aberrations(e.g., field curvature, axial chromatic, lateral chromatic, distortion,coma, and the like), which may cause the optics to focus rays toslightly different positions according to their wavelength or theirlateral positioning. In addition, the relative positions of the opticalsystem, the source array, and the grating, are selected according to themore general condition that the optical system images the grating atinfinity with respect to each source element of the light source array,at least for paraxial rays that emerge from each source.

Flow Diagram of One Example Embodiment

Reference is next being made to the flow diagram of FIG. 6 whichillustrates one example embodiment of the present method for determiningobjects in an image captured with a multi-band imaging system. Flowprocessing starts at 600 and immediately proceeds to step 602. In thisembodiment, pixels in the binary image have been classified according totwo materials of interest, i.e, human tissue and plastic, and thus haveall been assigned a binary value of 1. Pixels in the image which werenot classified as being either material of interest have been assigned avalue of 0. If the pixels in the pixel-classified binary image have beenassigned different binary values such as, for instance, a value of 1 forpixels classified as human tissue, and a value of 2 for pixelsclassified as plastic, then the present method would have to be repeatedonce to post-process the image to isolate blobs of pixels which havebeen classified as human tissue, and again to post-process the image toisolate blobs of pixels which have been classified as plastic.

At step 602, a binary image containing pixels which have been classifiedusing a pixel classification method is received. The source image wascaptured using an IR imaging system and then processed for pixelclassification according to a correlation method, a ratio method, or abest fitting reflectance method. Each pixel in the image has anassociated intensity value and each pixel has a pixel value of 1 or 0depending on whether that pixel was classified as being a material ofinterest or not.

At step 604, a block of pixels of size m×n is defined, where any of: m≧2and n≧2. FIG. 8 shows a portion 800 of the image of FIG. 1 wherein ablock 802 of pixels of size m×n has been defined. Preferably, a size ofa block is based upon an estimated size of objects likely to beidentified as comprising a material of interest within a field of viewof the imaging system used to capture the source image.

At step 606, a first block of pixels is selected for processing. Blocksmay be selected for processing starting, for example, from a first rowor column of the image and proceeding uniformly across rows and columns.Alternatively, the ordering of the blocks of pixels selected forprocessing is set by a user using, for instance, the graphical userinterface of the computer workstation of FIG. 13.

At step 608, a density value is calculated for the current block usingintensity values of pixels in this block. Methods for calculatingdensity values are well established. In one embodiment, the density fora given block comprises the mean of intensity values of pixels withinthat block. Intensity values of pixels may be combined to generate a newintensity value for one or more pixels within the block prior to thedensity value being calculated.

At step 610, the block's density is compared to a threshold value. Anexample threshold density threshold value is 0.55. The threshold can beadjusted as needed depending on the number of classification errors in agiven image.

At step 612, a determination is made whether the density for this blockis greater than the threshold.

Reference is now being made to the flow diagram of FIG. 7 which is acontinuation of the flow diagram of FIG. 6 with flow processingcontinuing with respect to either of nodes A or B.

If, at step 612, the block's density is greater than the threshold thenprocessing continues with respect to node A wherein, at step 614, allthe pixels in this block are set to 1. Otherwise, processing continueswith respect to node B wherein, at step 616, all the pixels in thisblock are set to 0. In either case, processing continues with respect tonode C wherein, at step 617, this block is shifted in the image by kpixels where k is less than either m or n. FIG. 8 shows block 802 havingbeen shifted (at 803) diagonally by k=3 pixels. A block can be shiftedin either the x or y directions, diagonally, or in a random direction,so long as the block is shifted by k pixels where k is less than eitherm or n.

At step 618, a determination is made whether any more blocks remain tobe processed. If so then processing continues with respect to node Dwherein, at step 606, a next block of pixels is selected and processingcontinues in a similar manner for this next block. A density iscalculated for this block based upon the intensity values of pixelswithin the block. This block's density is then compared to the thresholdand the values of pixels in this block are manipulated accordingly suchthat pixels within the block all have the same binary value. Once allthe blocks have been processed, then processing continues with respectto step 620.

At step 620, contiguously connected pixels having the same binary valueare grouped together to form objects. Thereafter, in this embodiment,further processing stops. In another embodiment, the formed objects arecounted. For example, if the material of interest is human tissue andthe formed objects are thus human occupants in a motor vehicle, then bycounting the number of objects the number of human occupants in themotor vehicle can be determined. Such an embodiment would find its usesin a vehicle occupancy detection system. In yet another embodiment,pixel intensity values are combined to generate a new intensity valuefor at least one pixel in a given block and the image post-processedaccordingly.

It should be understood that the flow diagrams depicted herein areillustrative. One or more of the operations illustrated in the flowdiagrams may be performed in a differing order. Other operations may beadded, modified, enhanced, or consolidated. Variations thereof areintended to fall within the scope of the appended claims. All orportions of the flow diagrams may be implemented partially or fully inhardware in conjunction with machine readable program instructions.

Performance Results

FIG. 9 shows the result of the present method applied to the binaryimage of FIG. 15 wherein pixels have been classified as to skin tissue.Notice that pixel classification errors have been reduced significantlywhen the pixel-classified image has been post-processed in accordancewith the present method.

FIG. 11 shows a binary version of the source image of FIG. 10 whereinpixels in the image having been classified as to skin tissue. FIG. 12shows a result of the present method applied to the pixel-classifiedbinary image of FIG. 11. Clearly, the number of pixel classificationerrors has been reduced significantly.

Block Diagram of Image Post-Processing System

Reference is now being made to FIG. 13 which illustrates a block diagramof an example image post-processing system wherein various aspects ofthe present post-processing method are performed in a manner asdescribed with respect to the flow diagrams of FIGS. 6 and 7.

In FIG. 13, the post-processing system 1300 is shown comprising, inpart, a networked computer workstation 1302 configured to perform any ofthe aspects of the present method. System 1302 includes a hard drive(internal to computer case 1304) which reads/writes to a machinereadable media 1306 such as a floppy disk, optical disk, CD-ROM, DVC,magnetic tape, etc. Case 1304 houses a motherboard with a processor andmemory, a communications link such as a network card, video card, andother software and hardware needed to perform the functionality of acomputing system. The system further includes a graphical user interfacecomprising a display device 1307 such as a CRT, LCD, touchscreen, andthe like, a keyboard 1308, and a mouse (not shown). Workstation 1302 hasan operating system and other specialized software configured to displaynumeric values, text, scroll bars, dials, slideable menu bars, pull-downmenus, selectable options, buttons, and the like, for entering,selecting, modifying, and accepting any information needed forprocessing. The embodiment shown is only illustrative and may includeany other functionality which any display device known in the arts iscapable of displaying. Software to configure a user interface or anyportion thereof to display/enter/accept data is generally customizable.Default settings and initialization parameters can be retrieved fromstorage device 1310, as needed. Although shown as a desktop computer, itshould be appreciated that workstation 1302 can be a laptop, amainframe, a client/server, or a special purpose computer such as anASIC, circuit board, dedicated processor, or the like.

Source image 1312 has been captured using a IR imaging system such asthat which is described with respect to FIGS. 3 and 4. Pixel Classifier1314 receives the source image and proceeds to classify pixels in thatimage with respect to materials of interest which have beenpre-determined or otherwise identified by a user. Database 1310, whichis in communication with Module 1314, may comprise a materials spectraldatabase depending on the pixel classification method utilized. Valuesmay be stored and/or retrieved from storage device 1310 as needed.Module 1314 outputs a pixel-classified binary image 1316. Block Shifter1318 retrieves a block 1334 of size m×n and outputs a block of pixels1320 for processing. One example block of pixels is shown in FIG. 8.Density Calculator 1322 receives the block of pixels and determines adensity based upon the intensity values of pixels within that block.Calculator 1322 outputs the block's density 1324 to Threshold Comparator1326 which receives the density value and compares that density to apre-determined threshold received from workstation 1302. The thresholdfor this block may have been provided by a user or retrieved fromstorage 1310. Each block may have a different threshold value.Comparator Module 1326 outputs a signal 1328 to Pixel Processor 1330 toeither set the values of all pixels in this block to one value in thoseinstances where the block's density was less than the threshold, or setthe values of all the pixels in this block to another value in theinstance where the density for this block was greater than thethreshold. Block densities which happen to be identical to the thresholdcan be set either way depending on a user's preference. Pixel Processor1330 writes the values of pixels in a block to memory device 1332wherein successive portions of the post-processed image are reassembled.Upon completion of the current block, processing returns to BlockShifter 1318 which shifts the block in a suitable direction along eitherthe x-axis, y-axis, diagonally, or in a random direction. Each iterationmay shift the block in a different direction. In various embodiments,Processor 1330 further processes the image by grouping togetherconnected pixels having a same binary value to form objects and countingthe number of formed objects in the image. The formed objects may bestored to device 1332. The image can then be provided to documentreproduction device 1334 for rendering or communicated over network 1301to a display device for subsequent viewing by a user or for furtherprocessing. It should be appreciated that some or all of thefunctionality described with respect to the modules and processors ofFIG. 13 may be performed, in whole or in part, within workstation 1302.Workstation 1302 is capable of communicating with any number of remotesystems over network 1301.

It should be appreciated that any of the modules and/or processors ofFIG. 13 are in communication with workstation 1302 and with storagedevices 1310 and 1332 via communication pathways (shown and not shown)and may store/retrieve data, parameter values, functions, pages,records, data, and machine readable/executable program instructionsrequired to perform their various functions. Connections between modulesincludes both physical and logical connections. Each may further be incommunication with one or more remote devices over network 1301.Connections between modules and processing units are intended to includeboth physical and logical connections. Various modules and processingunits described with respect to the system of FIG. 13 may designate oneor more components which may, in turn, comprise software and/or hardwaredesigned to perform a specific function. A plurality of modules mayperform a single function. A module may comprise a single piece ofhardware such as an ASIC, electronic circuit, or special purposeprocessor. A plurality of modules may be executed by either a singlespecial purpose computer system or a plurality of systems operating inparallel. Modules may include software/hardware modules which maycomprise an operating system, drivers, controllers, and otherapparatuses, some or all of which may be connected via network 1301.

Example Special Purpose Computer

Reference is now being made to FIG. 14 which illustrates a block diagramof one example special purpose computer for implementing one or moreaspects of the present method as described with respect to the flowdiagrams of FIGS. 6 and 7, and the various modules and processing unitsof the block diagram of FIG. 13. Such a special purpose processor iscapable of executing machine executable program instructions and maycomprise any of a micro-processor, micro-controller, ASIC, electroniccircuit, or any combination thereof.

Special purpose computer system 1400 includes processor 1406 forexecuting machine executable program instructions for carrying out allor some of the present method. The processor is in communication withbus 1402. The system includes main memory 1404 for storing machinereadable instructions. Main memory may comprise random access memory(RAM) to support reprogramming and flexible data storage. Buffer 1466stores data addressable by the processor. Program memory 1464 storesmachine readable instructions for performing the present method. Adisplay interface 1408 forwards data from bus 1402 to display 1410.Secondary memory 1412 includes a hard disk 1414 and storage device 1416capable of reading/writing to removable storage unit 1418, such as afloppy disk, magnetic tape, optical disk, etc. Secondary memory 1412 mayfurther include other mechanisms for allowing programs and/or machineexecutable instructions to be loaded onto the processor. Such mechanismsmay include, for example, a storage unit 1422 adapted to exchange datathrough interface 1420 which enables the transfer of software and data.The system includes a communications interface 1424 which acts as bothan input and an output to allow data to be transferred between thesystem and external devices such as a color scanner (not shown). Exampleinterfaces include a modem, a network card such as an Ethernet card, acommunications port, a PCMCIA slot and card, etc. Software and datatransferred via the communications interface are in the form of signals.Such signal may be any of electronic, electromagnetic, optical, or otherforms of signals capable of being received by the communicationsinterface. These signals are provided to the communications interfacevia channel 1426 which carries such signals and may be implemented usingwire, cable, fiber optic, phone line, cellular link, RF, memory, orother means known in the arts. The teachings hereof can be implementedin hardware or software using any known or later developed systems,structures, devices, and/or software by those skilled in the applicableart without undue experimentation from the functional descriptionprovided herein with a general knowledge of the relevant arts.

It is also contemplated that one or more aspects of the present methodmay be practiced in distributed computing environments where tasks areperformed by remote processing devices that are linked through acommunication network. In a distributed computing environment, programmodules for performing various aspects of the present system and method.Other embodiments include a special purpose computer designed to performthe methods disclosed herein. The methods described can be implementedon a special purpose computer, a micro-processor or micro-controller, anASIC or other integrated circuit, a DSP, an electronic circuit such as adiscrete element circuit, a programmable device such as a PLD, PLA,FPGA, PAL, PDA, and the like. The teachings hereof can be implemented inhardware or software using any known or later developed systems,structures, devices, and/or software by those skilled in the applicableart without undue experimentation from the functional descriptionprovided herein with a general knowledge of the relevant arts. Moreover,the methods hereof may be readily implemented as software executed on aprogrammed general purpose computer, a special purpose computer, amicroprocessor, or the like.

One or more aspects of the methods described herein are intended to beincorporated in an article of manufacture, including one or morecomputer program products, having computer usable or machine readablemedia. For purposes hereof, a computer usable or machine readable mediais, for example, a floppy disk, a hard-drive, memory, CD-ROM, DVD, tape,cassette, or other digital or analog media, or the like, which iscapable of having embodied thereon a computer readable program, one ormore logical instructions, or other machine executable codes or commandsthat implement and facilitate the function, capability, andmethodologies described herein. Furthermore, the article of manufacturemay be included on at least one storage device readable by a machinearchitecture or other xerographic or image processing system embodyingexecutable program instructions capable of performing the methodologydescribed in the flow diagrams. Additionally, the article of manufacturemay be included as part of an operating system, a plug-in, or may beshipped, sold, leased, or otherwise provided separately, either alone oras part of an add-on, update, upgrade, or product suite.

It will be appreciated that various of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be desirablycombined into many other different systems or applications. Variouspresently unforeseen or unanticipated alternatives, modifications,variations, or improvements therein may become apparent and/orsubsequently made by those skilled in the art, which are also intendedto be encompassed by the following claims. Accordingly, the embodimentsset forth above are considered to be illustrative and not limiting.Changes to the above-described embodiments may be made without departingfrom the spirit and scope of the invention. The teachings of any printedpublications including patents and patent applications, are eachseparately hereby incorporated by reference in their entirety.

What is claimed is:
 1. A method for post-processing a pixel-classifiedbinary image for determination of a number of objects in the imagecomprising a material of interest, the method comprising: receiving abinary image containing pixels which have been classified using a pixelclassification method, each pixel in said image has an intensity value,each of said pixels has a binary value depending on whether said pixelwas classified as a material of interest or not; defining a block ofpixels of size m×n, where any of: m≧2 and n≧2; (A) calculating a densityvalue using intensity values collected for pixels in a block of pixelsof said size, said block encompassing a current pixel; (B) comparingsaid density to a pre-determined threshold value; and (C) in response tosaid density being less than said threshold, setting all pixels in thisblock to zero, otherwise setting all pixels in this block to a non-zerovalue; and shifting said block by k pixels, where k is less than m or n;repeating steps (A)-(C) until all pixels in said image have beenprocessed; grouping together connected pixels having a same binary valueto form objects; and counting a number of said formed objects in saidimage.
 2. The method of claim 1, wherein said pixel classificationmethod comprises any of: a correlation method, a ratio method, and abest fitting reflectance method.
 3. The method of claim 1, whereincalculating said density comprises determining a mean of pixel intensityvalues.
 4. The method of claim 1, further comprising combining pixelintensity values to generate a new intensity value for at least onepixel in a given block.
 5. The method of claim 1, wherein pixels of saidimage have been classified as to a material of interest being humantissue, and said formed objects are human occupants in a motor vehicle.6. The method of claim 1, wherein a size of said block is selected basedupon an estimated size of objects likely to be identified as a materialof interest within a field of view of an imaging system used to capturesaid image.
 7. The method of claim 1, wherein said suitable direction isgiven by any of: along the x-axis, the y-axis, a diagonal, and a randomdirection.
 8. A system for post-processing a pixel-classified binaryimage for determination of a number of objects in the image comprising amaterial of interest, the system comprising: a memory and a storagedevice; and a processor in communication with said memory and storagedevice, said processor executing machine readable instructions forperforming: receiving a binary image containing pixels which have beenclassified using a pixel classification method, each pixel in said imagehas an intensity value, each of said pixels has a binary value dependingon whether said pixel was classified as a material of interest or not;defining a block of pixels of size m×n, where either: m≧2 or n≧2; (A)calculating a density value using intensity values collected for pixelsin a block of pixels of said size, said block encompassing a currentpixel; (B) comparing said density to a pre-determined threshold value;and (C) in response to said density being less than said threshold,setting all pixels in this block to zero, otherwise setting all pixelsin this block to a non-zero value; and shifting said block by k pixels,where k is less than m or n; repeating steps (A)-(C) until all pixels insaid image have been processed; grouping together connected pixelshaving a same binary value to form objects; and counting a number ofsaid formed objects in said image.
 9. The system of claim 8, whereinsaid pixel classification method comprises any of: a correlation method,a ratio method, and a best fitting reflectance method.
 10. The system ofclaim 8, wherein calculating said density comprises determining a meanof pixel intensity values.
 11. The system of claim 8, further comprisingcombining pixel intensity values to generate a new intensity value forat least one pixel in a given block.
 12. The system of claim 8, whereinpixels of said image have been classified as to a material of interestbeing human tissue, and said formed objects are human occupants in amotor vehicle.
 13. The system of claim 8, wherein a size of said blockis selected based upon an estimated size of objects likely to beidentified as a material of interest within a field of view of animaging system used to capture said image.
 14. The system of claim 8,wherein said suitable direction is given by any of: along the x-axis,the y-axis, a diagonal, and a random direction.
 15. A computerimplemented method for post-processing a pixel-classified binary imagefor determination of a number of objects in the image comprising amaterial of interest, the method comprising: receiving a binary imagecontaining pixels which have been classified using a pixelclassification method, each pixel in said image has an intensity value,each of said pixels has a binary value depending on whether said pixelwas classified as a material of interest or not; defining a block ofpixels of size m×n, where either: m≧2 or n≧2; (A) calculating a densityvalue using intensity values collected for pixels in a block of pixelsof said size, said block encompassing a current pixel; (B) comparingsaid density to a pre-determined threshold value; and (C) in response tosaid density being less than said threshold, setting all pixels in thisblock to zero, otherwise setting all pixels in this block to a non-zerovalue; and shifting said block by k pixels, where k is less than m or n;repeating steps (A)-(C) until all pixels in said image have beenprocessed; grouping together connected pixels having a same binary valueto form objects; and counting a number of said formed objects in saidimage.
 16. The computer implemented method of claim 15, wherein saidpixel classification method comprises any of: a correlation method, aratio method, and a best fitting reflectance method.
 17. The computerimplemented method of claim 15, wherein calculating said densitycomprises determining a mean of pixel intensity values.
 18. The computerimplemented method of claim 15, further comprising combining pixelintensity values to generate a new intensity value for at least onepixel in a given block.
 19. The computer implemented method of claim 15,wherein pixels of said image have been classified as to a material ofinterest being human tissue, and said formed objects are human occupantsin a motor vehicle.
 20. The computer implemented method of claim 15,wherein a size of said block is selected based upon an estimated size ofobjects likely to be identified as a material of interest within a fieldof view of an imaging system used to capture said image.
 21. Thecomputer implemented method of claim 15, wherein said suitable directionis given by any of: along the x-axis, the y-axis, a diagonal, and arandom direction.